Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2020WR028095 |
Evaluating the potential and challenges of an uncertainty quantification method for long short‐term memory models for soil moisture predictions | |
Kuai Fang; Daniel Kifer; Kathryn Lawson; Chaopeng Shen | |
2020-11-09 | |
发表期刊 | Water Resources Research |
出版年 | 2020 |
英文摘要 | Recently, recurrent deep networks have shown promise to harness newly available satellite‐sensed data for long‐term soil moisture projections. However, to be useful in forecasting, deep networks must also provide uncertainty estimates. Here we evaluated Monte Carlo dropout with an input‐dependent data noise term (MCD+N), an efficient uncertainty estimation framework originally developed in computer vision, for hydrologic time series predictions. MCD+N simultaneously estimates a heteroscedastic input‐dependent data noise term (a trained error model attributable to observational noise) and a network weight uncertainty term (attributable to insufficiently‐constrained model parameters). Although MCD+N has appealing features, many heuristic approximations were employed during its derivation, and rigorous evaluations and evidence of its asserted capability to detect dissimilarity were lacking. To address this, we provided an in‐depth evaluation of the scheme's potential and limitations. We showed that for reproducing soil moisture dynamics recorded by the Soil Moisture Active Passive (SMAP) mission, MCD+N indeed gave a good estimate of predictive error, provided that we tuned a hyperparameter and used a representative training dataset. The input‐dependent term responded strongly to observational noise, while the model term clearly acted as a detector for physiographic dissimilarity from the training data, behaving as intended. However, when the training and test data were characteristically different, the input‐dependent term could be misled, undermining its reliability. Additionally, due to the data‐driven nature of the model, data noise also influences network weight uncertainty. Hence the two uncertainty terms are correlated. This approach has promise, but care is needed to interpret the results. |
领域 | 资源环境 |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/303984 |
专题 | 资源环境科学 |
推荐引用方式 GB/T 7714 | Kuai Fang,Daniel Kifer,Kathryn Lawson,等. Evaluating the potential and challenges of an uncertainty quantification method for long short‐term memory models for soil moisture predictions[J]. Water Resources Research,2020. |
APA | Kuai Fang,Daniel Kifer,Kathryn Lawson,&Chaopeng Shen.(2020).Evaluating the potential and challenges of an uncertainty quantification method for long short‐term memory models for soil moisture predictions.Water Resources Research. |
MLA | Kuai Fang,et al."Evaluating the potential and challenges of an uncertainty quantification method for long short‐term memory models for soil moisture predictions".Water Resources Research (2020). |
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